Chen et al. [1]} use a very similar style loss to [2]}, but show that the content loss of [3]} can be omitted. Gu et al. [4]} add a soft constraint on the matches found by nearest neighbors encouraging each style vector to be used at most most \(k\)  times. Liao et al.  [5]} propose an algorithm for finding feature correspondences between the content and style images that begins by computing matches using neural features from a deep layer of the pretrained network, then spatially refines these matches by examining successively earlier layers until correspondences can be mapped to individual pixels. Their algorithm produces excellent results on style-content pairs with similar semantics and pose, but generally does not work for more disparate style-content pairs. Mechrez et al.  [6]} propose a modified nearest neighbors based loss, which increases the importance of matching content features with unique matches in the style image (e.g. matching a nose to a nose) and down-weights matches with many roughly equivalent neighbors (e.g. homogenous texture to homogenous texture). our proposed algorithm 'Neural Neighbor Style Transfer' (described in Chapter ) falls within this regime and shares many similarities with [1]}, but demonstrates how seemingly small design changes can lead to dramatically higher visual quality.
